Abstract
Function Optimization is a typical problem. A mixed crossover strategy genetic algorithm for function optimization is proposed in this paper. Four crossover strategies are mixed in this algorithm and the performance is improved compared with traditional genetic algorithm using single crossover strategy. The numerical experiment is carried out on nine traditional functions and the results show that the proposed algorithm is superior to four single pure crossover strategy genetic algorithms in the convergence rate for function optimization problems. © 2008 Springer-Verlag Berlin Heidelberg.
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CITATION STYLE
Zhuang, L. Y., Dong, H. B., Jiang, J. Q., & Song, C. Y. (2008). A genetic algorithm using a mixed crossover strategy. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5263 LNCS, pp. 854–863). Springer Verlag. https://doi.org/10.1007/978-3-540-87732-5_94
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